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Pytorch Question from 'Deep Reinforcement Learning: Hands-On'


By : Miran
Date : October 01 2020, 12:00 AM
I hope this helps . OurModule defined a PyTorch nn.Module that accepts 2 inputs (num_inputs) and produces 3 outputs (num_classes).
It consists of:
code :


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What is the difference between reinforcement learning and deep RL?


By : jeffrey
Date : March 29 2020, 07:55 AM
I wish this help you Reinforcement Learning
In reinforcement learning, an agent tries to come up with the best action given a state.
code :
state | action | Q(state, action)
---------------------------------
  ... |   ...  |   ...
Q = neural_network.predict(state, action)

Why and when is deep reinforcement learning needed instead of q-learning?


By : Vin SF
Date : March 29 2020, 07:55 AM
I wish this help you Q-learning is a model-free reinforcement learning method first documented in 1989. It is “model-free” in the sense that the agent does not attempt to model its environment. It arrives at a policy based on a Q-table which stores the result of taking any action from a given state. When the agent is in state s, it refers to the Q-table for the state and picks the action with the highest associated award. In order for the agent to arrive at an optimal policy, it must balance exploration of all available actions for all states with exploiting what the Q-table says is the optimal action for a given state. If the agent always picks a random action, it will never arrive at an optimal policy; likewise, if the agent always chooses the action with the highest estimated reward, it may arrive at a sub-optimal policy since certain state-action pairs may not have been completely explored.
Given enough time, Q-learning can eventually find an optimal policy π for any finite Markov decision process (MDP). In the example of a simple game of Tic-Tac-Toe, the number of total disparate game states is less than 6,000. That might sound like a high number, but consider a simple video game environment in OpenAI’s gym environment known as “Lunar Lander”.

What's the difference between reinforcement learning, deep learning, and deep reinforcement learning?


By : Mark
Date : March 29 2020, 07:55 AM
This might help you Reinforcement learning is about teaching an agent to navigate an environment using rewards. Q-learning is one of the primary reinforcement learning methods.
Deep learning uses neural networks to achieve a certain goal, such as recognizing letters and words from images.

Reinforcement Learning with Pytorch. [Error: KeyError ]


By : user2955560
Date : March 29 2020, 07:55 AM
Hope this helps I am new to reinforcement learning as well as pytorch. I am learning it from Udemy. However, the code I have is the same as it is shown but I am having an error. I guess it is a pytorch error but can't debug it. If anyone helps it is really appreciated. , The below code
code :
action = torch.max(random_values,1)[1][0]
new_state, reward, done, info = env.step(action.item())

How to do reinforcement learning with an LSTM in PyTorch?


By : Bruno Aquino Filardi
Date : March 29 2020, 07:55 AM
I wish did fix the issue. Maybe you can feed your input sequence in a loop to your LSTM. Something, like this:
code :
h, c = Variable(torch.zeros()), Variable(torch.zeros())
for i in range(T):
    input = Variable(...)
    _, (h, c) = lstm(input, (h,c))
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